Introduction and Practical Guide to RAG for Large Models

Introduction and Practical Guide to RAG for Large Models

by
Book Giveaway at the End
Since RAG was introduced by Facebook AI Research in 2020, it has rapidly gained popularity.
After all, it has truly been a great help, playing a key role in solving the “hallucination” problem of large language models.
Today, tech giants like Google, AWS, IBM, Microsoft, and NVIDIA are all supporting the development of RAG applications. Microsoft also open-sourced a graph-based question-answering system GraphRAG in July, which quickly gained a lot of stars on GitHub. Notion’s CEO Ivan Zhao boldly predicted: “I believe RAG will fundamentally change the way knowledge is managed, freeing people from tedious information organization.” NVIDIA’s Vice President of AI Software Kari Briski is also optimistic about its future: “As companies adopt these AI frameworks in 2023, we expect to hear more about RAG.”
To help students who want to enter the field of large model research get started smoothly, Xiao Yi has brought a comprehensive introductory and practical guide “Application Development for Large Models: Introduction and Practice of RAG”, which teaches you step-by-step how to build a fully functional RAG project from basic concepts to practical operations.
Introduction and Practical Guide to RAG for Large Models
▼Click below toget the book at half price, for the first 200 people
Follow the steps in the book to quickly light up your AI skill tree!

Part.1

Overview of RAG

Before we start, let’s clarify what RAG, which specifically addresses the shortcomings of large models, is all about.
RAG, which stands for Retrieval-Augmented Generation, is an important deep learning-based document search framework for large models. It enhances the model’s ability to handle knowledge-intensive tasks like question answering, text summarization, and content generation by retrieving relevant information from an external knowledge base and providing it as a prompt to the large language model (LLM), effectively avoiding the model’s tendency to produce nonsensical outputs.
So, how does the RAG model achieve this process? Its basic principle is to utilize deep learning techniques to represent and model documents, thereby enabling end-to-end processing of document retrieval and generation. To build such an efficient RAG system, we need three core components to work together: Retriever, Generator, and Ranker.
To elaborate further, each component works as follows:

· Retriever: This is the first stage of the RAG model, responsible for retrieving documents relevant to the query from a pool of candidate documents. It can utilize various retrieval techniques and algorithms, such as keyword matching and semantic similarity, to quickly filter out potentially relevant documents.

· Generator: This is the second stage of the RAG model, responsible for generating summaries or answers related to the query based on the retrieved candidate documents. It typically employs generative models, such as language models or Generative Adversarial Networks (GANs), to produce text in natural language.

· Ranker: This is the final stage of the RAG model, responsible for ranking and scoring the generated text to determine the final output order of the documents. It can utilize various ranking algorithms, such as machine learning and deep learning, to score and rank the documents.

The technical performance is indeed satisfying, and using it brings many benefits:

(1) Supports diverse search needs, including document retrieval, question answering, and summary generation.

(2) Supports multilingual and multimedia searches, including various forms of information such as text, images, audio, and video.

(3) Effectively utilizes deep learning techniques to represent and model documents, thereby improving the accuracy and relevance of search results.

(4) Provides personalized search services based on user queries and historical search records, enhancing the user search experience.

The RAG model not only sounds impressive but is also valuable in practice, for example:

· Application in enterprise knowledge management systems: Intelligent knowledge retrieval and sharing, intelligent Q&A and problem-solving, knowledge graph construction and intelligent recommendation, intelligence analysis and decision support;

· Application in online Q&A systems: Automatic Q&A and customer service, internal knowledge sharing and collaboration, educational and learning assistance;

· Application in intelligence retrieval systems: Rapid information retrieval and analysis, integration of diverse information resources, intelligence analysis and decision support.

……

RAG truly excels in providing efficient document search and Q&A services, and its key contributor is PyTorch, a highly flexible and powerful deep learning framework that helps developers easily handle complex model building and algorithm optimization, enhancing RAG’s performance.
If you want to dive deep into RAG, mastering the basics of PyTorch is essential. Although RAG is becoming increasingly popular, learning it well is still quite difficult because resources are scattered. Don’t worry, this book is your savior; it packages the knowledge of RAG and the programming skills of PyTorch, outlining an efficient learning path for you.
Now, let this book guide us in hands-on RAG development.

Part.2

Hands-On RAG Development

This book consists of 9 chapters, structured as “Basic Introduction — Technical Deep Dive — Practical Applications”, covering all aspects of RAG large model technology, including work content, technical details, and applications in various fields.
Introduction and Practical Guide to RAG for Large Models

Basic Introduction

This book first provides the big picture of AI and NLP, then focuses on how RAG technology excels in document search.
In Chapter 3, it thoroughly explains everything from perceptrons to multilayer perceptrons, convolutional neural networks, recurrent neural networks, and up to Transformers, BERT, and GPT.
In Chapter 4, it uses ChatGPT as an example to show how advanced text processing has become.

Technical Deep Dive

This book also covers a series of practical techniques, from document chunking and vectorization to using deep learning models for vector retrieval, and combining prompt techniques for precise responses, with easy-to-understand explanations and practical examples at each stage. It also introduces how to use tools like Streamlit and Gradio to create visually appealing web interfaces, allowing you to intuitively see the effects of RAG technology.
Introduction and Practical Guide to RAG for Large Models
(The book explains that PyTorch provides tools like torchsummary to help visualize model structures)

Practical Applications

This book is not just a textbook that talks theory without practice. From the foundational chapters, we learn while practicing.
For instance, Chapter 2 takes you hands-on, starting from the basics of PyTorch programming, gradually delving into the essence of deep learning. It not only covers how to install PyTorch but also basic skills like tensor operations and automatic differentiation, as well as practical techniques like model building and training evaluation, ensuring you can learn the essence of deep learning from theory to practice.
Introduction and Practical Guide to RAG for Large Models
When it comes to the RAG technology part, it details document chunking and vectorization techniques, as well as how to convert text into a format understandable by machines. For example, using bag-of-words, word embeddings, and even various methods like pre-trained models.
Introduction and Practical Guide to RAG for Large Models
Moving forward, it dives into RAG’s vector retrieval technology, explaining not only what vector retrieval is, but also how to calculate it, especially focusing on the Locality Sensitive Hashing (LSH) algorithm, which helps solve large-scale document retrieval problems. In Chapter 8, you will learn how to give instructions to large models, from instruction design to templates, and then to agent patterns and chain-of-thought prompts, all aimed at enabling more effective communication with large models and stimulating your creativity.
Introduction and Practical Guide to RAG for Large Models
Finally, it provides a hands-on project that teaches you how to implement a PDF reader, combining all the theories and techniques learned, allowing you to personally experience the powerful charm of RAG technology in practical applications.
Introduction and Practical Guide to RAG for Large Models

Part.3

Conclusion
The unique aspects of this book are:

① Comprehensive Coverage: It takes you from document extraction all the way to similarity search, covering every core aspect of document search to ensure you master the key technologies.

② In-Depth Analysis: Each aspect is not just superficially covered, but delves into the core of the technology, with practical cases showing how to apply it in projects.

③ Cutting-Edge Technology: Not only does it teach you the old ways, but it also introduces you to the application of cutting-edge technologies like large model Q&A in document search, keeping you abreast of technological trends.

④ Simple and Understandable: It uses plain language, avoiding professional jargon and complex mathematics, making it accessible for beginners.

Practical and easy to understand! Whether you are a search engine engineer looking to improve search efficiency and accuracy, a researcher exploring the cutting edge of NLP technology, a data scientist or analyst seeking new data analysis methods, or an enthusiast looking to expand your AI knowledge, or a document management professional hoping to improve document processing efficiency, you will find what you need in this book!
Introduction and Practical Guide to RAG for Large Models▼Click below toget the book at half price, for the first 200 people
—END—
Introduction and Practical Guide to RAG for Large Models

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